{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:W3DYZSW7NYEGUJAIUOI5PVZLGR","short_pith_number":"pith:W3DYZSW7","schema_version":"1.0","canonical_sha256":"b6c78ccadf6e086a2408a391d7d72b347fc62a15d048c604642694835e303c43","source":{"kind":"arxiv","id":"2505.03977","version":1},"attestation_state":"computed","paper":{"title":"Call for Action: towards the next generation of symbolic regression benchmark","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.NE"],"primary_cat":"cs.LG","authors_text":"Alcides Fonseca, Bogdan Burlacu, Fabr\\'icio Olivetti de Fran\\c{c}a, Geoffrey Bomarito, Guilherme S. Imai Aldeia, Hengzhe Zhang, Miles Cranmer, William G. La Cava","submitted_at":"2025-05-06T21:02:20Z","abstract_excerpt":"Symbolic Regression (SR) is a powerful technique for discovering interpretable mathematical expressions. However, benchmarking SR methods remains challenging due to the diversity of algorithms, datasets, and evaluation criteria. In this work, we present an updated version of SRBench. Our benchmark expands the previous one by nearly doubling the number of evaluated methods, refining evaluation metrics, and using improved visualizations of the results to understand the performances. Additionally, we analyze trade-offs between model complexity, accuracy, and energy consumption. Our results show t"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2505.03977","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-06T21:02:20Z","cross_cats_sorted":["cs.NE"],"title_canon_sha256":"7499d368b8e0119a512f6ef9a4defd9b99f91d5f440dd3f5584ea287a2932549","abstract_canon_sha256":"470a3b7b551f8cb7e21f0038f2b11b9f55427ba67c07490b3fc9fd4218dd2ab6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:59:34.563888Z","signature_b64":"l9sSbc2+xLPQtgtxDu4vo+qCqMuWVFX6FYocTNzK4bphOjrKW+fcBdOuYo5sN4jA+cafRc0MFrQZPg4WSGmsCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b6c78ccadf6e086a2408a391d7d72b347fc62a15d048c604642694835e303c43","last_reissued_at":"2026-07-05T10:59:34.563374Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:59:34.563374Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Call for Action: towards the next generation of symbolic regression benchmark","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.NE"],"primary_cat":"cs.LG","authors_text":"Alcides Fonseca, Bogdan Burlacu, Fabr\\'icio Olivetti de Fran\\c{c}a, Geoffrey Bomarito, Guilherme S. Imai Aldeia, Hengzhe Zhang, Miles Cranmer, William G. La Cava","submitted_at":"2025-05-06T21:02:20Z","abstract_excerpt":"Symbolic Regression (SR) is a powerful technique for discovering interpretable mathematical expressions. However, benchmarking SR methods remains challenging due to the diversity of algorithms, datasets, and evaluation criteria. In this work, we present an updated version of SRBench. Our benchmark expands the previous one by nearly doubling the number of evaluated methods, refining evaluation metrics, and using improved visualizations of the results to understand the performances. Additionally, we analyze trade-offs between model complexity, accuracy, and energy consumption. Our results show t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.03977","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2505.03977/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2505.03977","created_at":"2026-07-05T10:59:34.563439+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.03977v1","created_at":"2026-07-05T10:59:34.563439+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.03977","created_at":"2026-07-05T10:59:34.563439+00:00"},{"alias_kind":"pith_short_12","alias_value":"W3DYZSW7NYEG","created_at":"2026-07-05T10:59:34.563439+00:00"},{"alias_kind":"pith_short_16","alias_value":"W3DYZSW7NYEGUJAI","created_at":"2026-07-05T10:59:34.563439+00:00"},{"alias_kind":"pith_short_8","alias_value":"W3DYZSW7","created_at":"2026-07-05T10:59:34.563439+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2512.07961","citing_title":"Towards symbolic regression for interpretable clinical decision scores","ref_index":22,"is_internal_anchor":false},{"citing_arxiv_id":"2603.20910","citing_title":"LLM-ODE: Data-driven Discovery of Dynamical Systems with Large Language Models","ref_index":1,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/W3DYZSW7NYEGUJAIUOI5PVZLGR","json":"https://pith.science/pith/W3DYZSW7NYEGUJAIUOI5PVZLGR.json","graph_json":"https://pith.science/api/pith-number/W3DYZSW7NYEGUJAIUOI5PVZLGR/graph.json","events_json":"https://pith.science/api/pith-number/W3DYZSW7NYEGUJAIUOI5PVZLGR/events.json","paper":"https://pith.science/paper/W3DYZSW7"},"agent_actions":{"view_html":"https://pith.science/pith/W3DYZSW7NYEGUJAIUOI5PVZLGR","download_json":"https://pith.science/pith/W3DYZSW7NYEGUJAIUOI5PVZLGR.json","view_paper":"https://pith.science/paper/W3DYZSW7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.03977&json=true","fetch_graph":"https://pith.science/api/pith-number/W3DYZSW7NYEGUJAIUOI5PVZLGR/graph.json","fetch_events":"https://pith.science/api/pith-number/W3DYZSW7NYEGUJAIUOI5PVZLGR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/W3DYZSW7NYEGUJAIUOI5PVZLGR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/W3DYZSW7NYEGUJAIUOI5PVZLGR/action/storage_attestation","attest_author":"https://pith.science/pith/W3DYZSW7NYEGUJAIUOI5PVZLGR/action/author_attestation","sign_citation":"https://pith.science/pith/W3DYZSW7NYEGUJAIUOI5PVZLGR/action/citation_signature","submit_replication":"https://pith.science/pith/W3DYZSW7NYEGUJAIUOI5PVZLGR/action/replication_record"}},"created_at":"2026-07-05T10:59:34.563439+00:00","updated_at":"2026-07-05T10:59:34.563439+00:00"}